Learning About Autonomous Self-Regulation from Global Professionals: A Mixed Methods Study
Bibliographic record
Abstract
Adapting to rapid globalization, Japan struggles to change its education from enforcing conformity to enhancing autonomy. To find solutions, the author interviewed Japanese global professionals (GPs) who daringly worked abroad and developed autonomous self-regulation when others still believed in naturally following the tradition of lifetime employment. According to previous research, coping is a form of self-regulation, and one’s coping process strongly influences one’s autonomy. Thus, using the Process Model of Coping by Skinner & Edge, this study examined GPs’ coping processes by a qualitative method, Trajectory Equifinality Approach (TEA). In addition to the coping styles mentioned by Skinner & Edge, this study revealed qualities that contributed to GPs’ coping processes, and the base of all of their adaptations was a growth mindset. The qualitative results led to develop a survey instrument to gather quantitative data. The mixed methods results suggest that GPs’ autonomous self-regulation starts from a growth mindset that helps them take on challenges out of their comfort zones, or even countries of origin, and that multicultural environments with novel ideas and conflicts enhance the autonomous self-regulatory practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".